Peste, E.-A., Vladu, A., Kurtic, E., Lampert, C., &; Alistarh, D.-A. (n.d.). CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning Representations . Kigali, Rwanda .

Tomaszewska, P., &; Lampert, C. (2023). On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift. In International Workshop on Reproducible Research in Pattern Recognition (Vol. 14068, pp. 67–73). Montreal, Canada: Springer Nature.https://doi.org/10.1007/978-3-031-40773-4_6 

Súkeník, P., Mondelli, M., &; Lampert, C. (n.d.). Deep neural collapse is provably optimal for the deep unconstrained features model. In 37th Annual Conference on Neural Information Processing Systems. New Orleans, LA, United States.

Prach, B., &; Lampert, C. (n.d.). 1-Lipschitz neural networks are more expressive with N-activations. arXiv.https://doi.org/10.48550/ARXIV.2311.06103 

Scott, J. A., Yeo, M. X., &; Lampert, C. (n.d.). Cross-client Label Propagation for transductive federated learning. arXiv.https://doi.org/10.48550/arXiv.2210.06434 

Súkeník, P., &; Lampert, C. (n.d.). Generalization in Multi-objective machine learning. arXiv.https://doi.org/10.48550/arXiv.2208.13499 

Iofinova, E. B., Konstantinov, N. H., &; Lampert, C. (2022). FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research. ML Research Press.

Prach, B., &; Lampert, C. (2022). Almost-orthogonal layers for efficient general-purpose Lipschitz networks. In Computer Vision – ECCV 2022 (Vol. 13681, pp. 350–365). Tel Aviv, Israel: Springer Nature.https://doi.org/10.1007/978-3-031-19803-8_21 

Lampert, J., &; Lampert, C. (2022). Overcoming rare-language discrimination in multi-lingual sentiment analysis. In 2021 IEEE International Conference on Big Data (pp. 5185–5192). Orlando, FL, United States: IEEE.https://doi.org/10.1109/bigdata52589.2021.9672003 

Tomaszewska, P., &; Lampert, C. (2022). Lightweight conditional model extrapolation for streaming data under class-prior shift. In 26th International Conference on Pattern Recognition (Vol. 2022, pp. 2128–2134). Montreal, Canada: Institute of Electrical and Electronics Engineers.https://doi.org/10.1109/icpr56361.2022.9956195 

Konstantinov, N. H., &; Lampert, C. (2022). Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. ML Research Press.

Konstantinov, N. H., &; Lampert, C. (2022). On the impossibility of fairness-aware learning from corrupted data. In Proceedings of Machine Learning Research (Vol. 171, pp. 59–83). ML Research Press.

Volhejn, V., &; Lampert, C. (2021). Does SGD implicitly optimize for smoothness? In 42nd German Conference on Pattern Recognition (Vol. 12544, pp. 246–259). Tübingen, Germany: Springer.https://doi.org/10.1007/978-3-030-71278-5_18 

Phuong, M., &; Lampert, C. (2021). The inductive bias of ReLU networks on orthogonally separable data. In 9th International Conference on Learning Representations. Virtual.

Konstantinov, N. H., &; Lampert, C. (n.d.). Fairness through regularization for learning to rank. arXiv.https://doi.org/10.48550/arXiv.2102.05996 

Lampert, C. (2021). Zero-Shot Learning. In K. Ikeuchi (Ed.), Computer Vision (2nd ed., pp. 1395–1397). Cham: Springer.https://doi.org/10.1007/978-3-030-63416-2_874 

Anciukevicius, T., Lampert, C., &; Henderson, P. M. (n.d.). Object-centric image generation with factored depths, locations, and appearances. arXiv.

Henderson, P. M., &; Lampert, C. (2020). Unsupervised object-centric video generation and decomposition in 3D. In 34th Conference on Neural Information Processing Systems (Vol. 33, pp. 3106–3117). Vancouver, Canada: Curran Associates.

Royer, A., &; Lampert, C. (2020). Localizing grouped instances for efficient detection in low-resource scenarios. In IEEE Winter Conference on Applications of Computer Vision.https://doi.org/10.1109/WACV45572.2020.9093288__Snowmass Village, CO, United States: IEEE.https://doi.org/10.1109/WACV45572.2020.9093288 

Royer, A., &; Lampert, C. (2020). A flexible selection scheme for minimum-effort transfer learning. In 2020 IEEE Winter Conference on Applications of Computer Vision. Snowmass Village, CO, United States: IEEE.https://doi.org/10.1109/WACV45572.2020.9093635 

Phuong, M., &; Lampert, C. (2020). Functional vs. parametric equivalence of ReLU networks. In 8th International Conference on Learning Representations. Online.

Konstantinov, N. H., Frantar, E., Alistarh, D.-A., &; Lampert, C. (2020). On the sample complexity of adversarial multi-source PAC learning. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 5416–5425). Online: ML Research Press.

Henderson, P. M., Tsiminaki, V., &; Lampert, C. (2020). Leveraging 2D data to learn textured 3D mesh generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7498–7507). Virtual: IEEE.https://doi.org/10.1109/CVPR42600.2020.00752 

Sun, R., &; Lampert, C. (2020). KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications. International Journal of Computer Vision. Springer Nature.https://doi.org/10.1007/s11263-019-01232-x 

Kersting, K., Lampert, C., &; Rothkopf, C. (Eds.). (2019). Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt (1st ed.). Wiesbaden: Springer Nature.https://doi.org/10.1007/978-3-658-26763-6 

Ashok, P., Brázdil, T., Chatterjee, K., Ketínský, J., Lampert, C., &; Toman, V. (2019). Strategy representation by decision trees with linear classifiers. In 16th International Conference on Quantitative Evaluation of Systems (Vol. 11785, pp. 109–128). Glasgow, United Kingdom: Springer Nature.https://doi.org/10.1007/978-3-030-30281-8_7 

Xian, Y., Lampert, C., Schiele, B., &; Akata, Z. (2019). Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence. Institute of Electrical and Electronics Engineers (IEEE).https://doi.org/10.1109/tpami.2018.2857768 

Phuong, M., &; Lampert, C. (2019). Distillation-based training for multi-exit architectures. In IEEE International Conference on Computer Vision (Vol. 2019–October, pp. 1355–1364). Seoul, Korea: IEEE.https://doi.org/10.1109/ICCV.2019.00144 

Kolesnikov, A., Kuznetsova, A., Lampert, C., &; Ferrari, V. (2019). Detecting visual relationships using box attention. In Proceedings of the 2019 International Conference on Computer Vision Workshop. Seoul, South Korea: IEEE.https://doi.org/10.1109/ICCVW.2019.00217 

Phuong, M., &; Lampert, C. (2019). Towards understanding knowledge distillation. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 5142–5151). Long Beach, CA, United States: ML Research Press.

Konstantinov, N. H., &; Lampert, C. (2019). Robust learning from untrusted sources. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 3488–3498). Long Beach, CA, USA: ML Research Press.

Sun, R., &; Lampert, C. (2019). KS(conf): A light-weight test if a ConvNet operates outside of Its specifications (Vol. 11269, pp. 244–259). Presented at the GCPR: Conference on Pattern Recognition, Stuttgart, Germany: Springer Nature.https://doi.org/10.1007/978-3-030-12939-2_18 

Darrell, T., Lampert, C., Sebe, N., Wu, Y., &; Yan, Y. (2018). Guest editors’ introduction to the special section on learning with Shared information for computer vision and multimedia analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE.https://doi.org/10.1109/TPAMI.2018.2804998 

Uijlings, J., Konyushkova, K., Lampert, C., &; Ferrari, V. (2018). Learning intelligent dialogs for bounding box annotation. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9175–9184). Salt Lake City, UT, United States: IEEE.https://doi.org/10.1109/cvpr.2018.00956 

Sahoo, S., Lampert, C., &; Martius, G. S. (2018). Learning equations for extrapolation and control. In Proceedings of the 35th International Conference on Machine Learning (Vol. 80, pp. 4442–4450). Stockholm, Sweden: ML Research Press.

Kuzborskij, I., &; Lampert, C. (2018). Data-dependent stability of stochastic gradient descent. In Proceedings of the 35 th International Conference on Machine Learning (Vol. 80, pp. 2815–2824). Stockholm, Sweden: ML Research Press.

Martius, G. S., &; Lampert, C. (2017). Extrapolation and learning equations. In 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. Toulon, France: International Conference on Learning Representations.

Pielorz, J., Prandtstetter, M., Straub, M., &; Lampert, C. (2017). Optimal geospatial volunteer allocation needs realistic distances. In 2017 IEEE International Conference on Big Data (pp. 3760–3763). Boston, MA, United States: IEEE.https://doi.org/10.1109/BigData.2017.8258375 

Kolesnikov, A., &; Lampert, C. (2017). PixelCNN models with auxiliary variables for natural image modeling. In 34th International Conference on Machine Learning (Vol. 70, pp. 1905–1914). Sydney, Australia: JMLR.

Rebuffi, S. A., Kolesnikov, A., Sperl, G., &; Lampert, C. (2017). iCaRL: Incremental classifier and representation learning (Vol. 2017, pp. 5533–5542). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE.https://doi.org/10.1109/CVPR.2017.587 

Royer, A., Kolesnikov, A., &; Lampert, C. (2017). Probabilistic image colorization (p. 85.1-85.12). Presented at the BMVC: British Machine Vision Conference, London, United Kingdom: BMVA Press.https://doi.org/10.5244/c.31.85 

Zimin, A., &; Lampert, C. (2017). Learning theory for conditional risk minimization (Vol. 54, pp. 213–222). Presented at the AISTATS: Artificial Intelligence and Statistics, Fort Lauderdale, FL, United States: ML Research Press.

Pentina, A., &; Lampert, C. (2017). Multi-task learning with labeled and unlabeled tasks (Vol. 70, pp. 2807–2816). Presented at the ICML: International Conference on Machine Learning, Sydney, Australia: ML Research Press.

Kolesnikov, A., &; Lampert, C. (2016). Improving weakly-supervised object localization by micro-annotation. In Proceedings of the British Machine Vision Conference 2016 (Vol. 2016–September, p. 92.1-92.12). York, United Kingdom: BMVA Press.https://doi.org/10.5244/C.30.92 

Kolesnikov, A., &; Lampert, C. (2016). Seed, expand and constrain: Three principles for weakly-supervised image segmentation (Vol. 9908, pp. 695–711). Presented at the ECCV: European Conference on Computer Vision, Amsterdam, The Netherlands: Springer.https://doi.org/10.1007/978-3-319-46493-0_42 

Pielorz, J., &; Lampert, C. (2016). Optimal geospatial allocation of volunteers for crisis management. Presented at the ICT-DM: Information and Communication Technologies for Disaster Management, Rennes, France: IEEE.https://doi.org/10.1109/ICT-DM.2015.7402041 

Pentina, A., &; Lampert, C. (2015). Lifelong learning with non-i.i.d. tasks (Vol. 2015, pp. 1540–1548). Presented at the NIPS: Neural Information Processing Systems, Montreal, Canada: Neural Information Processing Systems.

Shah, N., Kolmogorov, V., &; Lampert, C. (2015). A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle (pp. 2737–2745). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA: IEEE.https://doi.org/10.1109/CVPR.2015.7298890 

Royer, A., &; Lampert, C. (2015). Classifier adaptation at prediction time (pp. 1401–1409). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE.https://doi.org/10.1109/CVPR.2015.7298746 

Lampert, C. (2015). Predicting the future behavior of a time-varying probability distribution (pp. 942–950). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE.https://doi.org/10.1109/CVPR.2015.7298696 

Pentina, A., Sharmanska, V., &; Lampert, C. (2015). Curriculum learning of multiple tasks (pp. 5492–5500). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE.https://doi.org/10.1109/CVPR.2015.7299188 

Muelling, K., Kroemer, O., Lampert, C., &; Schölkopf, B. (2014). Movement templates for learning of hitting and batting. In J. Kober &; J. Peters (Eds.), Learning Motor Skills (Vol. 97, pp. 69–82). Springer.https://doi.org/10.1007/978-3-319-03194-1_3 

Hernandez Lobato, D., Sharmanska, V., Kersting, K., Lampert, C., &; Quadrianto, N. (2014). Mind the nuisance: Gaussian process classification using privileged noise. In Advances in Neural Information Processing Systems (Vol. 1, pp. 837–845). Montreal, Canada: Neural Information Processing Systems.

Kolesnikov, A., Guillaumin, M., Ferrari, V., &; Lampert, C. (2014). Closed-form approximate CRF training for scalable image segmentation. In D. Fleet, T. Pajdla, B. Schiele, &; T. Tuytelaars (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8691, pp. 550–565). Zurich, Switzerland: Springer.https://doi.org/10.1007/978-3-319-10578-9_36 

Khamis, S., &; Lampert, C. (2014). CoConut: Co-classification with output space regularization. In Proceedings of the British Machine Vision Conference 2014. Nottingham, UK: BMVA Press.

Sydorov, V., Sakurada, M., &; Lampert, C. (2014). Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1402–1409). Columbus, USA: IEEE.https://doi.org/10.1109/CVPR.2014.182 

Pentina, A., &; Lampert, C. (2014). A PAC-Bayesian bound for Lifelong Learning (Vol. 32, pp. 991–999). Presented at the ICML: International Conference on Machine Learning, Beijing, China: ML Research Press.

Kazmar, T., Kvon, E., Stark, A., &; Lampert, C. (2013). Drosophila Embryo Stage Annotation using Label Propagation. Presented at the ICCV: International Conference on Computer Vision, Sydney, Australia: IEEE.https://doi.org/10.1109/ICCV.2013.139 

Sharmanska, V., Quadrianto, N., &; Lampert, C. (2013). Learning to rank using privileged information (pp. 825–832). Presented at the ICCV: International Conference on Computer Vision, Sydney, Australia: IEEE.https://doi.org/10.1109/ICCV.2013.107 

Lampert, C., Nickisch, H., &; Harmeling, S. (2013). Attribute-based classification for zero-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE.https://doi.org/10.1109/TPAMI.2013.140 

Chen, C., Kolmogorov, V., Yan, Z., Metaxas, D., &; Lampert, C. (2013). Computing the M most probable modes of a graphical model (Vol. 31, pp. 161–169). Presented at the  AISTATS: Conference on Uncertainty in Artificial Intelligence, Scottsdale, AZ, United States: JMLR.

Tommasi, T., Quadrianto, N., Caputo, B., &; Lampert, C. (2013). Beyond dataset bias: Multi-task unaligned shared knowledge transfer. Presented at the ACCV: Asian Conference on Computer Vision, Daejeon, Korea: Springer.https://doi.org/10.1007/978-3-642-37331-2_1 

Quadrianto, N., &; Lampert, C. (2013). Kernel based learning. In W. Dubitzky, O. Wolkenhauer, K. Cho, &; H. Yokota (Eds.), Encyclopedia of Systems Biology (Vol. 3, pp. 1069–1069). Springer.https://doi.org/10.1007/978-1-4419-9863-7_604 

Lampert, C. (2012). Dynamic pruning of factor graphs for maximum marginal prediction (Vol. 1, pp. 82–90). Presented at the NIPS: Neural Information Processing Systems, Lake Tahoe, NV, United States: Neural Information Processing Systems.

Blaschko, M., &; Lampert, C. (2012). Guest editorial: Special issue on structured prediction and inference. International Journal of Computer Vision. Springer.https://doi.org/10.1007/s11263-012-0530-y 

Sharmanska, V., Quadrianto, N., &; Lampert, C. (2012). Augmented attribute representations (Vol. 7576, pp. 242–255). Presented at the ECCV: European Conference on Computer Vision, Florence, Italy: Springer.https://doi.org/10.1007/978-3-642-33715-4_18 

Müller, A., Nowozin, S., &; Lampert, C. (2012). Information theoretic clustering using minimal spanning trees (Vol. 7476, pp. 205–215). Presented at the DAGM: German Association For Pattern Recognition, Graz, Austria: Springer.https://doi.org/10.1007/978-3-642-32717-9_21 

Lampert, C., &; Peters, J. (2012). Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components. Journal of Real-Time Image Processing. Springer.https://doi.org/10.1007/s11554-010-0168-3 

Korc, F., Kolmogorov, V., &; Lampert, C. (2012). Approximating marginals using discrete energy minimization. Presented at the ICML: International Conference on Machine Learning, Edinburgh, Scotland: ICML.

Korc, F., Kolmogorov, V., &; Lampert, C. (2012). Approximating marginals using discrete energy minimization. IST Austria.https://doi.org/10.15479/AT:IST-2012-0003 

Kroemer, O., Lampert, C., &; Peters, J. (2012). Multi-modal learning for dynamic tactile sensing. Deutsches Zentrum für Luft und Raumfahrt.

Quadrianto, N., Lampert, C., &; Chen, C. (2012). The most persistent soft-clique in a set of sampled graphs. In Proceedings of the 29th International Conference on Machine Learning (pp. 211–218). Edinburgh, United Kingdom: ML Research Press.

Wang, Z., Lampert, C., Mülling, K., Schölkopf, B., &; Peters, J. (2011). Learning anticipation policies for robot table tennis (pp. 332–337). Presented at the IROS: RSJ International Conference on Intelligent Robots and Systems, San Francisco, USA: IEEE.https://doi.org/10.1109/IROS.2011.6094892 

Blaschko, M., Shelton, J., Bartels, A., Lampert, C., &; Gretton, A. (2011). Semi supervised kernel canonical correlation analysis with application to human fMRI. Pattern Recognition Letters. Elsevier.https://doi.org/10.1016/j.patrec.2011.02.011 

Kroemer, O., Lampert, C., &; Peters, J. (2011). Learning dynamic tactile sensing with robust vision based training. IEEE Transactions on Robotics. IEEE.https://doi.org/10.1109/TRO.2011.2121130 

Chen, C., Freedman, D., &; Lampert, C. (2011). Enforcing topological constraints in random field image segmentation. IST Austria.https://doi.org/10.15479/AT:IST-2011-0002 

Chen, C., Freedman, D., &; Lampert, C. (2011). Enforcing topological constraints in random field image segmentation. In CVPR: Computer Vision and Pattern Recognition (pp. 2089–2096). Colorado Springs, CO, United States: IEEE.https://doi.org/10.1109/CVPR.2011.5995503 

Lampert, C. (2011). Maximum margin multi-label structured prediction. Presented at the NIPS: Neural Information Processing Systems, Granada, Spain: Neural Information Processing Systems.

Lampert, C. (2011). Maximum margin multi label structured prediction. NIPS: Neural Information Processing Systems. Neural Information Processing Systems Foundation.

Nowozin, S., &; Lampert, C. (2011). Structured learning and prediction in computer vision. Foundations and Trends in Computer Graphics and Vision. Now Publishers.https://doi.org/10.1561/0600000033 

Quadrianto, N., &; Lampert, C. (2011). Learning multi-view neighborhood preserving projections (pp. 425–432). Presented at the ICML: International Conference on Machine Learning, Bellevue, United States: ML Research Press.

Nowozin, S., &; Lampert, C. (2010). Global interactions in random field models: A potential function ensuring connectedness. SIAM Journal on Imaging Sciences. Society for Industrial and Applied Mathematics .https://doi.org/10.1137/090752614 

Tang, K., Tappen, M., Sukthankar, R., &; Lampert, C. (2010). Optimizing one-shot recognition with micro-set learning (pp. 3027–3034). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE.https://doi.org/10.1109/CVPR.2010.5540053 

Kober, J., Mülling, K., Krömer, O., Lampert, C., Schölkopf, B., &; Peters, J. (2010). Movement templates for learning of hitting and batting (pp. 853–858). Presented at the ICRA: International Conference on Robotics and Automation, IEEE.https://doi.org/10.1109/ROBOT.2010.5509672 

Lampert, C., &; Krömer, O. (2010). Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning (Vol. 6312, pp. 566–579). Presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece: Springer.https://doi.org/10.1007/978-3-642-15552-9_41 

Wanke, J., Ulges, A., Lampert, C., &; Breuel, T. (2010). Topic models for semantic video compression (pp. 275–284). Presented at the MIR: Multimedia Information Retrieval, ACM.https://doi.org/10.1145/1743384.1743433 

Tuytelaars, T., Lampert, C., Blaschko, M., &; Buntine, W. (2010). Unsupervised object discovery: A comparison. International Journal of Computer Vision. Springer.https://doi.org/10.1007/s11263-009-0271-8 

Lampert, C. (2010). An efficient divide-and-conquer cascade for nonlinear object detection (pp. 1022–1029). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE.https://doi.org/10.1109/CVPR.2010.5540107 

Nowozin, S., Gehler, P., &; Lampert, C. (2010). On parameter learning in CRF-based approaches to object class image segmentation (Vol. 6316, pp. 98–111). Presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece: Springer.https://doi.org/10.1007/978-3-642-15567-3_8 

Blaschko, M., Lampert, C., &; Bartels, A. (2009). Semi-supervised analysis of human fMRI data. BBCI: Berlin Brain-Computer Interface Workshop - Advances in Neurotechnology. Berlin Institute of Technology.

Blaschko, M., &; Lampert, C. (2009). Object localization with global and local context kernels (pp. 1–11). Presented at the BMVC: British Machine Vision Conference, BMVA Press.https://doi.org/10.5244/C.23.63 

Lampert, C., Nickisch, H., &; Harmeling, S. (2009). Learning to detect unseen object classes by between-class attribute transfer (pp. 951–958). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE.https://doi.org/10.1109/CVPR.2009.5206594 

Lampert, C., &; Peters, J. (2009). Active structured learning for high-speed object detection (Vol. 5748, pp. 221–231). Presented at the DAGM: German Association For Pattern Recognition, Springer.https://doi.org/10.1007/978-3-642-03798-6_23 

Lampert, C., &; Peters, J. (2009). A high-speed object tracker from off-the-shelf components. ICCV: International Conference on Computer Vision. IEEE.

Lampert, C., &; Blaschko, M. (2009). Structured prediction by joint kernel support estimation. Machine Learning. Springer.https://doi.org/10.1007/s10994-009-5111-0 

Dhillon, P., Nowozin, S., &; Lampert, C. (2009). Combining appearance and motion for human action classification in videos (pp. 22–29). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE.https://doi.org/10.1109/CVPRW.2009.5204237 

Lampert, C., Blaschko, M., &; Hofmann, T. (2009). Efficient subwindow search: A branch and bound framework for object localization. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE.https://doi.org/10.1109/TPAMI.2009.144 

Dhillon, P., Nowozin, S., &; Lampert, C. (2009). Combining appearance and motion for human action classification in videos (pp. 22–29). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE.https://doi.org/10.1109/CVPRW.2009.5204237 

Lampert, C. (2009). Kernel Methods in Computer Vision (Vol. 4). now publishers.https://doi.org/10.1561/0600000027 

Nowozin, S., &; Lampert, C. (2009). Global connectivity potentials for random field models (pp. 818–825). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE.https://doi.org/10.1109/CVPR.2009.5206567 

Lampert, C. (2009). Detecting objects in large image collections and videos by efficient subimage retrieval (pp. 987–994). Presented at the ICCV: International Conference on Computer Vision, IEEE.https://doi.org/10.1109/ICCV.2009.5459359 

Blaschko, M., &; Lampert, C. (2008). Learning to localize objects with structured output regression (Vol. 5302, pp. 2–15). Presented at the ECCV: European Conference on Computer Vision, Springer.https://doi.org/10.1007/978-3-540-88682-2_2 

Lampert, C. (2008). Partitioning of image datasets using discriminative context information (pp. 1–8). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE.https://doi.org/10.1109/CVPR.2008.4587448 

Lampert, C., &; Blaschko, M. (2008). A multiple kernel learning approach to joint multi-class object detection (Vol. 5096, pp. 31–40). Presented at the DAGM: German Association For Pattern Recognition, Springer.https://doi.org/10.1007/978-3-540-69321-5_4 

Lampert, C., Blaschko, M., &; Hofmann, T. (2008). Beyond sliding windows: Object localization by efficient subwindow search (pp. 1–8). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE.https://doi.org/10.1109/CVPR.2008.4587586 

Blaschko, M., Lampert, C., &; Gretton, A. (2008). Semi-supervised Laplacian regularization of kernel canonical correlation analysis (Vol. 5211, pp. 133–145). Presented at the ECML: European Conference on Machine Learning, Springer.https://doi.org/10.1007/978-3-540-87479-9_27 

Goldstein, M., Lampert, C., Reif, M., Stahl, A., &; Breuel, T. (2008). Bayes optimal DDoS mitigation by adaptive history-based IP filtering (pp. 174–179). Presented at the ICN: International Conference on Networking, IEEE.https://doi.org/10.1109/ICN.2008.64 

Lampert, C., &; Blaschko, M. (2008). Joint kernel support estimation for structured prediction (pp. 1–4). Presented at the NIPS SISO: NIPS Workshop on “Structured Input - Structured Output,” Curran Associates, Inc.

Blaschko, M., &; Lampert, C. (2008). Correlational spectral clustering (pp. 1–8). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE.https://doi.org/10.1109/CVPR.2008.4587353 

Blaschko, M., Hofmann, T., &; Lampert, C. (2007). Efficient subwindow search for object localization. Unknown. Max-Planck-Institute for Biological Cybernetics.

Ulges, A., Lampert, C., Keysers, D., &; Breuel, T. (2007). Optimal dominant motion estimation using adaptive search of transformation space (Vol. 4713, pp. 204–213). Presented at the DAGM: German Association For Pattern Recognition, Springer.https://doi.org/10.1007/978-3-540-74936-3_21 

Ulges, A., Lampert, C., Keysers, D., &; Breuel, T. (2007). Optimal dominant motion estimation using adaptive search of transformation space (Vol. 4713, pp. 204–213). Presented at the DAGM: German Association For Pattern Recognition, Springer.https://doi.org/10.1007/978-3-540-74936-3_21 

Lampert, C., &; Breuel, T. (2006). Objective quality measurement for geometric document image restoration. Presented at the DAS: Document Analysis Systems, Springer.

Lampert, C. (2006). Machine learning for video compression: Macroblock mode decision (pp. 936–940). Presented at the ICPR: International Conference on Pattern Recognition, IEEE.https://doi.org/10.1109/ICPR.2006.778 

Ali, H., Lampert, C., &; Breuel, T. (2006). Satellite tracks removal in astronomical images (Vol. 4225, pp. 892–901). Presented at the CIARP: Iberoamerican Congress in Pattern Recognition, Springer.https://doi.org/10.1007/11892755_92 

Ulges, A., Lampert, C., &; Keysers, D. (2006). Spatiogram-based shot distances for video retrieval (pp. 1–10). Presented at the TRECVID Workshop, NIST (National Institute of Standards and Technology, US Department of Commerce).

Lampert, C., Mei, L., &; Breuel, T. (2006). Printing technique classification for document counterfeit detection (Vol. 1, pp. 639–634). Presented at the CIS: Computational Intelligence and Security, IEEE.https://doi.org/10.1109/ICCIAS.2006.294214 

Lampert, C., &; Wirjadi, O. (2006). An optimal non-orthogonal separation of the anisotropic Gaussian convolution filter. IEEE Transactions on Image Processing (TIP). IEEE.https://doi.org/ 10.1109/TIP.2006.877501  

Lampert, C., &; Wirjadi, O. (2006). Anisotropic Gaussian filtering using fixed point arithmetic (pp. 1565–1568). Presented at the ICIP: IEEE International Conference on Image Processing, IEEE.https://doi.org/10.1109/ICIP.2006.312606 

Keysers, D., Lampert, C., &; Breuel, T. (2006). Color image dequantization by constrained diffusion (Vol. 6058). Presented at the SPIE Electronic Imaging, SPIE.https://doi.org/10.1117/12.648713 

Ulges, A., Lampert, C., &; Breuel, T. (2005). Document image dewarping using robust estimation of curled text lines (Vol. 2, pp. 1001–1005). Presented at the ICDAR: International Conference on Document Analysis and Recognition, IEEE.https://doi.org/ 10.1109/ICDAR.2005.90 

Lampert, C., Braun, T., Ulges, A., Keysers, D., &; Breuel, T. (2005). Oblivious document capture and real-time retrieval (pp. 79–86). Presented at the CBDAR: Camera Based Document Analysis and Recognition , CBDAR.

Lampert, C. (2005). Boundary regularity of admissible operators. Publicacions Matemàtiques. Universitat Autònoma de Barcelona, Departament de Matemàtique.https://doi.org/10.5565/PUBLMAT_49105_08 

Ulges, A., Lampert, C., &; Breuel, T. (2004). Document capture using stereo vision (pp. 198–200). Presented at the DocEng: ACM Symposium on Document Engineering, ACM.https://doi.org/10.1145/1030397.1030434 

Lampert, C. (2003). The Neumann operator in strictly pseudoconvex domains with weighted Bergman metric . Bonner Mathematische Schriften. Universität Bonn, Fachbibliothek Mathematik.